Learning When to Ask for Help: Efficient Interactive Navigation via Implicit Uncertainty Estimation (2305.16502v3)

Abstract: Robots operating alongside humans often encounter unfamiliar environments that make autonomous task completion challenging. Though improving models and increasing dataset size can enhance a robot's performance in unseen environments, data collection and model refinement may be impractical in every environment. Approaches that utilize human demonstrations through manual operation can aid in refinement and generalization, but often require significant data collection efforts to generate enough demonstration data to achieve satisfactory task performance. Interactive approaches allow for humans to provide correction to robot action in real time, but intervention policies are often based on explicit factors related to state and task understanding that may be difficult to generalize. Addressing these challenges, we train a lightweight interaction policy that allows robots to decide when to proceed autonomously or request expert assistance at estimated times of uncertainty. An implicit estimate of uncertainty is learned via evaluating the feature extraction capabilities of the robot's visual navigation policy. By incorporating part-time human interaction, robots recover quickly from their mistakes, significantly improving the odds of task completion. Incorporating part-time interaction yields an increase in success of 0.38 with only a 0.3 expert interaction rate within the Habitat simulation environment using a simulated human expert. We further show success transferring this approach to a new domain with a real human expert, improving success from less than 0.1 with an autonomous agent to 0.92 with a 0.23 human interaction rate. This approach provides a practical means for robots to interact and learn from humans in real-world settings.

Efficient Interactive Navigation via Implicit Uncertainty Estimation

The paper "Learning When to Ask for Help: Efficient Interactive Navigation via Implicit Uncertainty Estimation" explores a novel approach to enhancing robotic navigation in diverse and unfamiliar environments by integrating human interaction into the decision-making process. The authors aim to address the persistent challenge faced by autonomous robots in executing tasks within environments that differ significantly from the conditions they were trained under. While improvements in models and data aggregation can somewhat mitigate these issues, they are often impractical in dynamic and novel settings where robots operate alongside humans.

Main Contributions

The authors propose a light-weight, interactive policy that empowers a navigation robot to assess its own uncertainty implicitly and autonomously seek human assistance when needed. This approach contrasts with existing methods that rely heavily on explicit state or task understanding, which can be challenging to generalize across different environments.

The core mechanism introduced is a secondary policy that decides between autonomous continuation and soliciting human guidance based on an implicitly learned uncertainty model. This model is derived from the feature extraction capabilities of a pre-trained visual navigation policy, thus leveraging the existing computational framework without significant overhead.

Experimental Validation

The research validates the proposed method in both synthetic and real-world environments. It utilizes the Habitat simulation environment with a simulated expert to quantify improvements, demonstrating a substantial 0.38 increase in success rate with only a 0.3 interaction rate. The success is further echoed in a real domain, achieving a success rate climb from less than 0.1 in fully autonomous operation to 0.92 with a modest 0.23 interaction rate, highlighting the applicability and effectiveness of the human-augmented strategy.

Implications and Future Directions

This work holds significant implications in several dimensions of AI and robotic applications. Practically, it offers a robust framework for deploying robots in real-world environments without the exhaustive need for environment-specific data fine-tuning. This capability is critical for scenarios like search and rescue, where the environment cannot be fully known or simulated in advance.

Theoretically, the introduction of uncertainty-awareness as a trigger for human-robot interaction contributes to a deeper understanding of how autonomous systems can dynamically integrate human expertise in a feedback loop. This could spur further research into other domains of rational human-robot collaboration, potentially extending to multi-agent systems and more complex decision-making processes.

Looking forward, leveraging natural language interfaces as a medium for interaction and refining human-robot communication through few-shot learning approaches could extend the versatility of this strategy. Additionally, integrating this approach with more sophisticated strategies to pre-train and fine-tune navigation policies will likely enhance the robustness of robots against domain shifts, promoting a seamless transition from simulated to real-world environments.

In summary, the paper lays a significant foundation for the development of more adaptable and human-assistive robotic systems, facilitating growing synergies between AI and human capabilities in tangible, real-world tasks.